Diagnostic Reasoning in General Internal Medicine Consultations: A Text Mining Analysis of Consultation Requests

Diagnostic reasoning is often difficult to evaluate because it is largely internal, and linguistic features associated with consultation quality remain poorly understood. Consultation requests may provide a useful window into real-time diagnostic thinking. We used text mining to examine how language in consultation requests reflects diagnostic reasoning in general internal medicine.

We conducted a single-center retrospective observational study of diagnostic consultation requests from the Department of Diagnostic and Generalist Medicine at a Japanese university hospital in 2021. Consultations were grouped using an outcome-based operational definition of appropriateness, centered on whether the first specialty department established the correct final diagnosis, with an additional concordance requirement when a specific diagnosis was strongly suspected. Text mining using KH Coder included co-occurrence network analysis, correspondence analysis, and keyword-in-context (KWIC) concordance analysis.

Among 352 consultations, 229 were classified as appropriate and 123 as inappropriate. Appropriate consultations were characterized by terms indicating clear abnormalities and disease progression, whereas inappropriate consultations were associated with terms reflecting limited diagnostic direction, such as "unknown" and "negative". KWIC analysis showed two uses of "negative": revising the differential diagnosis after negative results and maintaining suspicion despite negative findings. In inappropriate consultations, these patterns were accompanied by potentially insufficient assessment/documentation and diagnostic uncertainty.

Text mining of consultation requests revealed linguistic features associated with consultation appropriateness and showed that "negative" reflected distinct diagnostic reasoning moves. Consultation request text may provide a scalable window into diagnostic reasoning and identify opportunities to improve diagnostic communication and consultation quality.